Interpretable machine learning models to predict the resistance of breast cancer patients to doxorubicin from their microRNA profiles
Author(s)
Ogunleye, Adeolu Z
Piyawajanusorn, Chayanit
Goncalves, Anthony
Ghislat, Ghita
Ballester, Pedro J
Type
Journal Article
Abstract
Doxorubicin is a common treatment for breast cancer. However, not all patients respond to this drug, which sometimes causes life-threatening side effects. Accurately anticipating doxorubicin-resistant patients would therefore permit to spare them this risk while considering alternative treatments without delay. Stratifying patients based on molecular markers in their pretreatment tumors is a promising approach to advance toward this ambitious goal, but single-gene gene markers such as HER2 expression have not shown to be sufficiently predictive. The recent availability of matched doxorubicin-response and diverse molecular profiles across breast cancer patients permits now analysis at a much larger scale. 16 machine learning algorithms and 8 molecular profiles are systematically evaluated on the same cohort of patients. Only 2 of the 128 resulting models are substantially predictive, showing that they can be easily missed by a standard-scale analysis. The best model is classification and regression tree (CART) nonlinearly combining 4 selected miRNA isoforms to predict doxorubicin response (median Matthew correlation coefficient (MCC) and area under the curve (AUC) of 0.56 and 0.80, respectively). By contrast, HER2 expression is significantly less predictive (median MCC and AUC of 0.14 and 0.57, respectively). As the predictive accuracy of this CART model increases with larger training sets, its update with future data should result in even better accuracy.
Date Issued
2022-08-25
Date Acceptance
2022-07-03
Citation
Advanced Science, 2022, 9 (24)
ISSN
2198-3844
Publisher
Wiley
Journal / Book Title
Advanced Science
Volume
9
Issue
24
Copyright Statement
© 2022 The Authors. Advanced Science published by Wiley-VCH GmbH
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000820231700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
Subjects
ANTICANCER
artificial intelligence
Chemistry
Chemistry, Multidisciplinary
CHEMOTHERAPY
DRUG RESPONSE
EXPRESSION PROFILE
GENOMIC MARKERS
machine learning
Materials Science
Materials Science, Multidisciplinary
multiomics
Nanoscience & Nanotechnology
PHARMACOGENOMIC BIOMARKERS
Physical Sciences
precision oncology
Science & Technology
Science & Technology - Other Topics
SENSITIVITY
SYSTEMATIC IDENTIFICATION
Technology
THERAPY
TOPOISOMERASE-II-ALPHA
tumor profiling
Publication Status
Published
Article Number
ARTN 2201501
Date Publish Online
2023-07-03